"""
Advanced event handlers with safe access patterns for large learner data.

This module provides specialized event handler implementations that demonstrate
safe patterns for accessing large data structures from learners without causing
performance issues in the event system.
"""

from typing import Any, Dict, Optional, List
import logging
import weakref

from learners.interfaces.event import EventHandler, EventContext, EventType


class SafeDataAccessHandler(EventHandler):
    """
    Event handler that safely accesses large data structures through controlled methods.

    Instead of directly accessing large attributes through the learner instance in
    the event context (which might be blocked by EventContext's lazy loading mechanism), this handler
    uses accessor methods that the learner provides specifically for event handlers.

    This pattern allows event handlers to access necessary data without requiring
    the full learner instance to be passed in the event context.
    """

    def __init__(self, name: str = "SafeDataAccessHandler"):
        """Initialize the handler."""
        self.name = name
        self._logger = logging.getLogger(self.__class__.__name__)
        self.events_to_handle = {EventType.ON_TRAIN_BATCH_END}

    def handle(self, context: EventContext) -> Optional[EventContext]:
        """
        Safely handle an event by accessing data through dedicated methods.

        Instead of directly accessing learner.network or learner.dataset, it calls
        methods like learner.get_network_statistics() that are designed to return
        lightweight summaries of the data.
        """
        if not context.learner:
            self._logger.warning("No learner available in context")
            return context

        # Get learner class to check if it supports our accessor methods
        learner_class = context.get("learner_class", "Unknown")

        try:
            # Check for safe accessor methods
            if hasattr(context.learner, "get_network_statistics"):
                # Use the safe accessor method instead of direct attribute access
                stats = context.learner.get_network_statistics()
                self._logger.info(f"Network statistics: {stats}")
            else:
                self._logger.info(f"Learner {learner_class} does not support network statistics")

            # Process other data safely...

            # Return the context to continue the chain
            return context

        except Exception as e:
            self._logger.error(f"Error processing event: {e}")
            return context

    def can_handle(self, event: EventType) -> bool:
        """Check if this handler can handle the given event type."""
        return event in self.events_to_handle


class DataRequestingHandler(EventHandler):
    """
    Event handler that explicitly requests specific data from the learner.

    This handler demonstrates a pattern where the handler requests specific data
    by name through a standard interface, rather than directly accessing attributes.

    It relies on the learner implementing a get_data_for_event method that can
    provide the requested data in a controlled, optimized format.
    """

    def __init__(self, name: str = "DataRequestingHandler"):
        """Initialize the handler with a specific data need."""
        self.name = name
        self._logger = logging.getLogger(self.__class__.__name__)
        self.required_data = ["model_state", "optimizer_state"]

    def handle(self, context: EventContext) -> Optional[EventContext]:
        """Handle an event by explicitly requesting needed data."""
        if not context.learner:
            self._logger.warning("No learner available in context")
            return context

        try:
            # Request specific data by name instead of accessing attributes directly
            if hasattr(context.learner, "get_data_for_event"):
                # This method would be implemented by the learner to provide
                # only what's needed in an optimized format
                data = context.learner.get_data_for_event(
                    context.event, self.required_data
                )

                if data:
                    self._logger.info(f"Received requested data: {list(data.keys())}")
                    # Process the data...
                else:
                    self._logger.info("No data received for request")
            else:
                self._logger.info("Learner does not support data requests")

            return context

        except Exception as e:
            self._logger.error(f"Error requesting data: {e}")
            return context

    def can_handle(self, event: EventType) -> bool:
        """This handler handles all events."""
        return True


class LearnerExtension:
    """
    A mixin class that learners can implement to provide safe data access patterns.

    This class demonstrates how learners can implement methods that provide
    controlled, optimized access to their data for event handlers, without
    requiring direct attribute access.
    """

    def get_network_statistics(self) -> Dict[str, Any]:
        """
        Get lightweight statistics about the network without passing the full model.

        Instead of returning the full network (which might be very large),
        this method returns only summary statistics.
        """
        stats = {}

        # Safe access to _network attribute if it exists
        if hasattr(self, "_network"):
            network = getattr(self, "_network")

            # Compute statistics instead of returning the full network
            if isinstance(network, dict) and "weights" in network:
                weights = network["weights"]
                stats["num_weights"] = len(weights)
                if weights:
                    stats["mean_weight"] = sum(weights) / len(weights)
                    stats["min_weight"] = min(weights)
                    stats["max_weight"] = max(weights)

        return stats

    def get_data_for_event(self, event_type: EventType, data_keys: List[str]) -> Dict[str, Any]:
        """
        Get specific data needed by event handlers based on explicit requests.

        Args:
            event_type: Type of event being handled
            data_keys: List of data keys that the handler needs

        Returns:
            Dictionary containing only the requested data
        """
        result = {}

        # Process each requested key
        for key in data_keys:
            # Map requested keys to actual data, possibly with transformations
            if key == "model_state":
                # Return a lightweight summary instead of the full model
                if hasattr(self, "_network"):
                    result[key] = {"size": "10MB", "layers": 12}  # Just an example

            elif key == "optimizer_state":
                # Return optimizer stats
                if hasattr(self, "_optimizer"):
                    result[key] = {"learning_rate": 0.001}  # Just an example

            # Add more mappings as needed...

        return result
